Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy
A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from thr...
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MDPI AG
2025-07-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/11/7/759 |
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| author | Jasciane da Silva Alves Bruna Parente de Carvalho Pires Luana Ferreira dos Santos Tiffany da Silva Ribeiro Kerry Brian Walsh Ederson Akio Kido Sergio Tonetto de Freitas |
| author_facet | Jasciane da Silva Alves Bruna Parente de Carvalho Pires Luana Ferreira dos Santos Tiffany da Silva Ribeiro Kerry Brian Walsh Ederson Akio Kido Sergio Tonetto de Freitas |
| author_sort | Jasciane da Silva Alves |
| collection | DOAJ |
| description | A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. |
| format | Article |
| id | doaj-art-e25e64c556854dc7a96dde9080db6d36 |
| institution | DOAJ |
| issn | 2311-7524 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj-art-e25e64c556854dc7a96dde9080db6d362025-08-20T02:45:56ZengMDPI AGHorticulturae2311-75242025-07-0111775910.3390/horticulturae11070759Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR SpectroscopyJasciane da Silva Alves0Bruna Parente de Carvalho Pires1Luana Ferreira dos Santos2Tiffany da Silva Ribeiro3Kerry Brian Walsh4Ederson Akio Kido5Sergio Tonetto de Freitas6Biosciences Center, Federal University of Pernambuco, RENORBIO, Recife 50670-901, PE, BrazilBrazilian Agricultural Research Corporation, Embrapa, Petrolina 56302-970, PE, BrazilBrazilian Agricultural Research Corporation, Embrapa, Petrolina 56302-970, PE, BrazilBrazilian Agricultural Research Corporation, Embrapa, Petrolina 56302-970, PE, BrazilInstitute for Future Farming Systems, Central Queensland University, Rockhampton, QLD 4702, AustraliaBiosciences Center, Federal University of Pernambuco, RENORBIO, Recife 50670-901, PE, BrazilBrazilian Agricultural Research Corporation, Embrapa, Petrolina 56302-970, PE, BrazilA method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes.https://www.mdpi.com/2311-7524/11/7/759classification modelsWEKAmachine learningspectroscopy |
| spellingShingle | Jasciane da Silva Alves Bruna Parente de Carvalho Pires Luana Ferreira dos Santos Tiffany da Silva Ribeiro Kerry Brian Walsh Ederson Akio Kido Sergio Tonetto de Freitas Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy Horticulturae classification models WEKA machine learning spectroscopy |
| title | Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy |
| title_full | Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy |
| title_fullStr | Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy |
| title_full_unstemmed | Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy |
| title_short | Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy |
| title_sort | non destructive detection of current internal disorders and prediction of future appearance in mango fruit using portable vis nir spectroscopy |
| topic | classification models WEKA machine learning spectroscopy |
| url | https://www.mdpi.com/2311-7524/11/7/759 |
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